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https://github.com/bogdan-kulynych/trials
Tiny Bayesian A/B testing library
https://github.com/bogdan-kulynych/trials
Last synced: 2 months ago
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Tiny Bayesian A/B testing library
- Host: GitHub
- URL: https://github.com/bogdan-kulynych/trials
- Owner: bogdan-kulynych
- License: mit
- Created: 2014-06-10T01:33:44.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2018-10-16T07:41:01.000Z (over 6 years ago)
- Last Synced: 2024-08-04T04:05:27.889Z (6 months ago)
- Language: Python
- Homepage:
- Size: 669 KB
- Stars: 77
- Watchers: 7
- Forks: 19
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- starred-awesome - trials - Tiny Bayesian A/B testing library (Python)
README
trials
======
Tiny Bayesian A/B testing library
[](https://travis-ci.org/bogdan-kulynych/trials) [](https://landscape.io/github/bogdan-kulynych/trials/master)
## Installation
Install system dependencies (Debian):
```
sudo apt-get install libatlas-dev libatlas-base-dev liblapack-dev gfortran
```
Install the Python package:
```
pip install git+git://github.com/bogdan-kulynych/trials.git@master
```
Run the tests:
```
nosetests trials/tests
```
## Usage
Import package
```python
from trials import Trials
```
Start a split test with Bernoulli (binary) observations
```python
test = Trials(['A', 'B', 'C'])
```
Observe successes and failures
```python
test.update({
'A': (50, 10), # 50 successes, 10 failures, total 60
'B': (75, 15), # 75 successes, 15 failures, total 90
'C': (20, 15) # 20 successes, 15 failures, total 35
})
```
Evaluate some statistics
```python
dominances = test.evaluate('dominance', control='A') # Dominance probabilities P(X > A)
lifts = test.evaluate('expected lift', control='A') # Expected lifts E[(X-A)/A]
intervals = test.evaluate('lift CI', control='A', level=95) # Lifts' 95%-credible intervals
```
Available statistics for Bernoulli observation variations: `expected posterior`, `posterior CI`, `expected lift`, `lift CI`, `empirical lift`, `dominance`, `z-test dominance`.
Print or visualize results
```python
for variation in ['B', 'C']:
print('Variation {name}:'.format(name=variation))
print('* E[lift] = {value:.2%}'.format(value=lifts[variation]))
print('* P({lower:.2%} < lift < {upper:.2%}) = 95%' \
.format(lower=intervals[variation][0], upper=intervals[variation][2]))
print('* P({name} > {control}) = {value:.2%}' \
.format(name=variation, control='A', value=dominances[variation]))
```
Examine the output:
```
Variation B:
* E[lift] = 0.22% # expected lift
* P(-13.47% < lift < 17.31%) = 95% # lift CI
* P(B > A) = 49.27% # dominance
Variation C:
* E[lift] = -31.22%
* P(-51.33% < lift < -9.21%) = 95%
* P(C > A) = 0.25%
```
#### Interpreting and analyzing results
As per the output above there's 50% chance that variation **B** is better than **A** (*dominance*). Most likely it is better by about 0.2% (*expected lift*), but there's 95% chance that real lift is anywhere betwen -13% to 17% (*lift CI*). You need more data to know if **B** is better or worse for sure.
There's 100% - 0.25% = 99.75% chance that variation **C** is worse than **A**. Most likely it is worse by about 31%, and there's 95% chance that real lift falls betwen -51% to -9%. The data was sufficient to tell that this variation is almost certainly inferior to both **A** and **B**. However, if this 99.75% chance still doesn't convince you, you need more data.
## Theory
Explanation of mathematics behind and usage guide are coming soon as a blog post.
Meanwhile, see the [notebook](http://nbviewer.ipython.org/github/bogdan-kulynych/trials/blob/master/examples/benchmark.ipynb) for comparison of Bayesian lift (blue) and empirical lift (green) errors in a theoretical benchmark with equal sample sizes. Bayesian approach is a little better at predicting the lift, but no miracles here. Bayesian p-values and frequentist (z-test) p-values yield almost identical results.